Generation of Novelty Ground Truth Image Using Image Classification and Semantic Segmentation for Copy-Move Forgery Detection

Since the ground truth (GT) generated by CNN has pieces of patch information of the learned class, the accurate detection of Copy-Move is ambiguous. With various CNNs for image classification and semantic segmentation, the generated GT images are different yet similar to patch patterns for detecting forgery regions. It is difficult to determine which network model-generated GT image is suitable. Therefore, an optimal GT image is essential in image forensics. The proposed scheme in this paper generates a novelty GT image to solve this problem for the correct detection of Copy-Move forgery. The novelty GT image was configured using image classification and semantic segmentation. The variety of GT images is generated by adopting the state-of-the-art four image classifications and one semantic segmentation in the deep neural network. The proposed scheme implements mainly three tasks: 1) each network model generates the GT images (GT net ), 2) which are convergence synthesized into one (GT conv ), and 3) it decomposed again into GT images (GT decomp ) with a threshold value of the 'Threshold Filter.' Here, the GTnet images involve two pieces of information about the image classification and semantic segmentation of the forgery image. The GTconv has two pieces of information as one GT image. The GTdecomp is decomposed GTconv into various GT images by the threshold value, which is a permeated degree of the information about 'Image classification' and 'Semantic segmentation.' The proposed novelty GT image is accomplished with this operational flow for Copy-Move forgery detection. The results confirmed in the experiment for comparing the performance of the existing GT net image, and the GT decomp image of the proposed scheme showed that the Accuracy and F1 Score of the proposed scheme had the maximum improvement rate of 0.4% and 0.2%, respectively. Also, by estimating the proposed CMFD scheme, Area Under the Curve (AUC) is graded as ' Excellent ( A )' with a value of 0.9 higher.